用扩散模型特征识别假图并溯源，轻量高效且泛化强。
Who Made This? Fake Detection and Source Attribution with Diffusion Features
- 基于预训练扩散模型各层特征，无需训练即可检测假图。
- 在GenImage上实现跨生成器检测的顶尖性能，仅需少量数据。
- 适合需要快速部署的AI图像溯源与内容安全场景。
生成模型的迅猛发展使得合成图像逼真度极高，真实与虚假内容的界限日益模糊。随着新模型不断涌现，检测系统必须超越简单的真假判断，具备对未见过的生成器和合成内容的强泛化能力。我们提出FRIDA（通过扩散特征分析进行假图像识别与源模型溯源），一个轻量、数据高效的框架，利用预训练Stable Diffusion模型的特征来检测并溯源AI生成图像。通过对不同生成器的数据在扩散U-Net各层中的编码方式深入分析，我们提出一种方法：(i) 采用无训练的k近邻策略检测合成图像；(ii) 通过紧凑神经分类器实现源模型归属。在GenImage基准测试中，FRIDA以极少数据实现了当前最优的跨生成器检测性能，同时保持了强大的源模型溯源能力。这些结果确立了扩散特征在AI生成图像取证中的可靠性。
原文摘要 · Abstract (English)
The rapid rise of generative models has yielded synthetic images of striking realism, blurring the line between real and fake content. As novel models proliferate, detectors must go beyond mere fake identification to robustly generalise across unseen generators and synthetic content. We introduce FRIDA (Fake image Recognition and source Identification via Diffusion features Analysis), a lightweight, data-efficient framework that uses features from a pre-trained Stable Diffusion Model to detect and attribute AI-generated images. Through an in-depth analysis of how data from different generators are encoded across diffusion U-Net layers, we propose a method that (i) detects synthetic images using a training-free $k$-Nearest Neighbour approach and (ii) performs source model attribution via a compact neural classifier. On the GenImage benchmark, FRIDA achieves state-of-the-art cross-generator detection with limited data while maintaining robust source model attribution capabilities. These results establish diffusion features as a reliable framework for AI-generated image forensics.
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